Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states.
In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents.
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages.
Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance.
1 code implementation • 21 Apr 2021 • Ren Yang, Radu Timofte, Jing Liu, Yi Xu, Xinjian Zhang, Minyi Zhao, Shuigeng Zhou, Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy, Xin Li, Fanglong Liu, He Zheng, Lielin Jiang, Qi Zhang, Dongliang He, Fu Li, Qingqing Dang, Yibin Huang, Matteo Maggioni, Zhongqian Fu, Shuai Xiao, Cheng Li, Thomas Tanay, Fenglong Song, Wentao Chao, Qiang Guo, Yan Liu, Jiang Li, Xiaochao Qu, Dewang Hou, Jiayu Yang, Lyn Jiang, Di You, Zhenyu Zhang, Chong Mou, Iaroslav Koshelev, Pavel Ostyakov, Andrey Somov, Jia Hao, Xueyi Zou, Shijie Zhao, Xiaopeng Sun, Yiting Liao, Yuanzhi Zhang, Qing Wang, Gen Zhan, Mengxi Guo, Junlin Li, Ming Lu, Zhan Ma, Pablo Navarrete Michelini, Hai Wang, Yiyun Chen, Jingyu Guo, Liliang Zhang, Wenming Yang, Sijung Kim, Syehoon Oh, Yucong Wang, Minjie Cai, Wei Hao, Kangdi Shi, Liangyan Li, Jun Chen, Wei Gao, Wang Liu, XiaoYu Zhang, Linjie Zhou, Sixin Lin, Ru Wang
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge.
The process of continuous integration/deployment (CICD) in cloud connects developers who need to deliver value faster and more transparently with site reliability engineers (SREs) who need to manage applications reliably.
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.
The paper aims at removing the aliasing effects for the whole focal stack generated from a sparse 3D light field, while keeping the consistency across all the focal layers. We first explore the structural characteristics embedded in the focal stack slice and its corresponding frequency-domain representation, i. e., the focal stack spectrum (FSS).
We derive the chiral effective Lagrangian for excited heavy-light mesons from QCD under proper approximations.
High Energy Physics - Phenomenology
Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets.
The low energy constants in the effective Lagrangian are expressed in terms of the light quark self-energy and heavy quark mass $m_Q$.
High Energy Physics - Phenomenology
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).
In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR.
One of the strengths of traditional convolutional neural networks (CNNs) is their inherent translational invariance.
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
2 code implementations • 27 Sep 2020 • Majed El Helou, Ruofan Zhou, Sabine Süsstrunk, Radu Timofte, Mahmoud Afifi, Michael S. Brown, Kele Xu, Hengxing Cai, Yuzhong Liu, Li-Wen Wang, Zhi-Song Liu, Chu-Tak Li, Sourya Dipta Das, Nisarg A. Shah, Akashdeep Jassal, Tongtong Zhao, Shanshan Zhao, Sabari Nathan, M. Parisa Beham, R. Suganya, Qing Wang, Zhongyun Hu, Xin Huang, Yaning Li, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Densen Puthussery, Hrishikesh P. S, Melvin Kuriakose, Jiji C. V, Yu Zhu, Liping Dong, Zhuolong Jiang, Chenghua Li, Cong Leng, Jian Cheng
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently.
In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data.
Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence.
We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets).
Ranked #1 on Node Classification on NELL
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning.
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs.
Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation.
no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples.
We prove that the light field is a 2D series, thus, a specifically designed CNN-LSTM network is proposed to capture the continuity property of the EPI.
In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely.
Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.
Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model.
no code implementations • 9 Mar 2018 • Gregory Kiar, Robert J. Anderson, Alex Baden, Alexandra Badea, Eric W. Bridgeford, Andrew Champion, Vikram Chandrashekhar, Forrest Collman, Brandon Duderstadt, Alan C. Evans, Florian Engert, Benjamin Falk, Tristan Glatard, William R. Gray Roncal, David N. Kennedy, Jeremy Maitin-Shepard, Ryan A. Marren, Onyeka Nnaemeka, Eric Perlman, Sharmishtaas Seshamani, Eric T. Trautman, Daniel J. Tward, Pedro Antonio Valdés-Sosa, Qing Wang, Michael I. Miller, Randal Burns, Joshua T. Vogelstein
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
no code implementations • • Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Yang Zheng, Lei Han, Haobo Fu, Xiangru Lian, Carson Eisenach, Haichuan Yang, Emmanuel Ekwedike, Bei Peng, Haoyue Gao, Tong Zhang, Ji Liu, Han Liu
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities.
To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation settings, by assuming independent items (i. e., arms).
In this paper, we propose a graph-based recursive neural network framework for collective vertex classification.
[Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.
The geometry of the recovered scene structure is affected by the calibration of the plenoptic camera significantly.
The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately.
Ranked #8 on Person Re-Identification on SYSU-30k (using extra training data)
In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics (i. e. the assigned labels of the target and the references).